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Pansharpening via sparsity optimization using overcomplete transforms.
- Source :
- 2013 IEEE International Geoscience & Remote Sensing Symposium - IGARSS; 2013, p856-859, 4p
- Publication Year :
- 2013
-
Abstract
- In this paper we consider pansharpening of multispectral satellite imagery based on solving an under-determined inverse problem regularized by the ℓ1-norm of the coefficients of overcomplete multi-scale transforms which all are tight-frame systems. There are two main approaches in sparsity promoting ℓ1-norm regularization, the analysis and the synthesis approach. We perform a number of experiments using two real and well known datasets where the focus is the comparison of the two approaches. One dataset includes a high resolution reference image while the other needs to be degraded prior to pansharpening in order to use the original multispectral image as the reference. Experiments are performed for a range of values for the regularization parameter, where each resulting pansharpened image is evaluated using three quality metrics. The behavior of those metrics as a function of the regularization parameter is compared for the analysis and synthesis formulations and it is shown that analysis gives better results. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISBNs :
- 9781479911141
- Database :
- Complementary Index
- Journal :
- 2013 IEEE International Geoscience & Remote Sensing Symposium - IGARSS
- Publication Type :
- Conference
- Accession number :
- 94535085
- Full Text :
- https://doi.org/10.1109/IGARSS.2013.6721294